This enhanced analysis examines acid mine drainage (AMD) chemistry data from the Cerro Rico mining area collected during 2006-2007. The study provides comprehensive insights into seasonal variations in metal concentrations, pH levels, environmental risks, and associated impacts on water quality and ecosystem health.
Key Findings: - Extreme acidification with pH values ranging from 0.5 to 12 - Severe metal contamination exceeding regulatory standards - Significant seasonal variations in metal loading - Critical environmental risks requiring immediate intervention
Setup and Enhanced Configuration
Load Required Libraries
Code
# Core data manipulation and visualizationlibrary(tidyverse)library(readxl)library(janitor)library(patchwork)library(scales)library(DT)library(kableExtra)library(plotly)# Statistical analysislibrary(broom)library(corrplot)library(cluster)# Data quality and explorationlibrary(skimr)# Spatial and temporal analysislibrary(lubridate)# Advanced visualizationlibrary(RColorBrewer)library(viridis)library(ggridges)# File handlinglibrary(here)# Set global optionsoptions(scipen =999, digits =4)knitr::opts_chunk$set(fig.retina =2, dpi =300)
# Identify available columnsavailable_metals <-detect_metal_columns(amd)
✓ Detected 12 metal columns:
# A tibble: 12 × 4
column metal confidence priority
<chr> <chr> <dbl> <dbl>
1 al al 1 1
2 as as 1 1
3 cd cd 1 1
4 cr cr 1 1
5 cu cu 1 1
6 fe fe 1 1
7 mn mn 1 1
8 pb pb 1 1
9 zn zn 1 1
10 co co 1 2
11 ni ni 1 2
12 q_l_s s 0.8 2
if (nrow(metal_summary) >0) {# Create comprehensive compliance report compliance_summary <- metal_summary %>%filter(!is.na(drinking_water_mcl)) %>%select(metal, max, drinking_water_mcl, aquatic_life_acute, aquatic_life_chronic, pct_exceed_drinking, pct_exceed_aquatic_acute, pct_exceed_aquatic_chronic) %>%mutate(drinking_exceedance = max / drinking_water_mcl,aquatic_acute_exceedance =ifelse(!is.na(aquatic_life_acute), max / aquatic_life_acute, NA),aquatic_chronic_exceedance =ifelse(!is.na(aquatic_life_chronic), max / aquatic_life_chronic, NA),risk_level =case_when( drinking_exceedance >100~"Critical", drinking_exceedance >10~"High", drinking_exceedance >1~"Moderate",TRUE~"Low" ) ) %>%arrange(desc(drinking_exceedance))cat("Regulatory Compliance Summary:\n")cat("==================================\n")# Critical violations critical_violations <- compliance_summary %>%filter(risk_level =="Critical")if (nrow(critical_violations) >0) {cat("🚨 CRITICAL VIOLATIONS (>100x drinking water standard):\n")for (i in1:nrow(critical_violations)) {cat("• ", critical_violations$metal[i], ": ", round(critical_violations$drinking_exceedance[i], 1), "x drinking water standard\n", sep ="") }cat("\n") }# High risk violations high_risk <- compliance_summary %>%filter(risk_level =="High")if (nrow(high_risk) >0) {cat("⚠️ HIGH RISK VIOLATIONS (10-100x drinking water standard):\n")for (i in1:nrow(high_risk)) {cat("• ", high_risk$metal[i], ": ", round(high_risk$drinking_exceedance[i], 1), "x drinking water standard\n", sep ="") }cat("\n") }# Aquatic life impacts aquatic_impacts <- compliance_summary %>%filter(!is.na(aquatic_chronic_exceedance) & aquatic_chronic_exceedance >1) %>%arrange(desc(aquatic_chronic_exceedance))if (nrow(aquatic_impacts) >0) {cat("🐟 AQUATIC LIFE IMPACTS:\n")for (i in1:min(5, nrow(aquatic_impacts))) {cat("• ", aquatic_impacts$metal[i], ": ", round(aquatic_impacts$aquatic_chronic_exceedance[i], 1), "x chronic aquatic standard\n", sep ="") }cat("\n") }# Sample exceedance rates high_exceedance <- compliance_summary %>%filter(!is.na(pct_exceed_drinking) & pct_exceed_drinking >50) %>%arrange(desc(pct_exceed_drinking))if (nrow(high_exceedance) >0) {cat("📊 HIGH EXCEEDANCE RATES (>50% of samples):\n")for (i in1:nrow(high_exceedance)) {cat("• ", high_exceedance$metal[i], ": ", round(high_exceedance$pct_exceed_drinking[i], 1), "% of samples exceed drinking water standard\n", sep ="") } }} else {cat("No regulatory compliance analysis available - insufficient metal data.\n")}
Regulatory Compliance Summary:
==================================
🚨 CRITICAL VIOLATIONS (>100x drinking water standard):
• fe: 240333x drinking water standard
• as: 88900x drinking water standard
• al: 37400x drinking water standard
• cd: 13060x drinking water standard
• mn: 8040x drinking water standard
• zn: 3920x drinking water standard
• pb: 2320x drinking water standard
• cu: 238.5x drinking water standard
⚠️ HIGH RISK VIOLATIONS (10-100x drinking water standard):
• cr: 25.1x drinking water standard
🐟 AQUATIC LIFE IMPACTS:
• fe: 72100x chronic aquatic standard
• cd: 261.2x chronic aquatic standard
• zn: 163.3x chronic aquatic standard
• al: 86x chronic aquatic standard
• cu: 34.4x chronic aquatic standard
📊 HIGH EXCEEDANCE RATES (>50% of samples):
• mn: 100% of samples exceed drinking water standard
• al: 96% of samples exceed drinking water standard
• cd: 92% of samples exceed drinking water standard
• zn: 88% of samples exceed drinking water standard
• pb: 88% of samples exceed drinking water standard
• fe: 84% of samples exceed drinking water standard
• as: 70% of samples exceed drinking water standard
• cu: 64% of samples exceed drinking water standard
cat("• Quality Assurance: Implement duplicate sampling and certified reference materials\n")
• Quality Assurance: Implement duplicate sampling and certified reference materials
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# Data quality recommendationscat("\nDATA QUALITY RECOMMENDATIONS\n")
DATA QUALITY RECOMMENDATIONS
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cat("=============================\n")
=============================
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cat("• Standardize sampling protocols across all sites\n")
• Standardize sampling protocols across all sites
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cat("• Implement quality control measures (blanks, duplicates, spikes)\n")
• Implement quality control measures (blanks, duplicates, spikes)
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cat("• Use certified reference materials for method validation\n")
• Use certified reference materials for method validation
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cat("• Document metadata for all samples (weather, flow conditions, etc.)\n")
• Document metadata for all samples (weather, flow conditions, etc.)
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cat("\n=== ENHANCED ANALYSIS COMPLETE ===\n")
=== ENHANCED ANALYSIS COMPLETE ===
Conclusions
This enhanced analysis of the Cerro Rico acid mine drainage data reveals significant environmental contamination requiring immediate attention. The systematic approach used here provides a framework for ongoing monitoring and assessment of mining-related water quality impacts.
Key Technical Improvements Made: - Robust error handling for missing data files - Enhanced data validation and quality assessment - Comprehensive statistical analysis with effect size calculations - Regulatory compliance evaluation against multiple standards - Advanced visualization techniques for complex datasets
Next Steps: 1. Implement real-time monitoring systems 2. Develop site-specific treatment strategies 3. Establish long-term ecological monitoring programs 4. Create community engagement and education initiatives